Line-based and plane-based icon research for automotive user interfaces

Area of Science:

• Human-Computer Interaction
• Cognitive Psychology
• Automotive Engineering

Background:

• Icons are crucial for overcoming language barriers in human-machine interaction, particularly in autonomous driving.
• Limited research exists on icon design principles for autonomous vehicles, with inconsistent classification methods and a focus on preference over cognitive mechanisms.
• Cognitive load theory provides a framework for understanding how visual features of icons affect user performance.

Purpose of the Study:

• To classify icons based on visual features relevant to autonomous driving interfaces.
• To investigate the impact of these visual features on icon recognition and memory load.
• To provide empirical evidence for optimizing icon design in automotive user interfaces.

Main Methods:

• Utilized electroencephalography (EEG) in a rapid interaction experiment with 43 participants.
• Collected data on icon recognition accuracy and memory load across different icon categories.
• Classified icons based on distinct visual features (e.g., line-based vs. plane-based).

Main Results:

• Visual features did not significantly affect icon recognition accuracy.
• Visual features significantly influenced memory load.
• Line-based icons were more effective under low memory load conditions.
• Plane-based icons showed advantages in high memory load tasks.

Conclusions:

• Icon design in autonomous driving should consider cognitive load, not just visual appeal or attention.
• Tailoring icon visual features to expected memory load can enhance user experience and performance.
• Findings offer practical guidance for designing effective automotive user interfaces.